---
title: "From RAG to Memory — paper by Gutierrez et al. (2025)"
description: "Updating an LLM's knowledge through fine-tuning or model editing is expensive and prone to… 45 references, cited by 88 papers here."
authors: ["Bernal Jimenez Gutierrez", "Yiheng Shu", "Weijian Qi", "Sizhe Zhou", "Yu Su"]
year: 2025
venue: "arXiv:2502.14802"
source: "https://arxiv.org/abs/2502.14802"
doi: "10.48550/arxiv.2502.14802"
arxiv: "2502.14802"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/gutierrez-2025-rag-memory-non-parametric-continual/"
---
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# From RAG to Memory: Non-Parametric Continual Learning for Large Language Models

**Authors:** [Bernal Jimenez Gutierrez](https://md.research.elamur.ai/free/en/authors/bernal-jimenez-gutierrez/), [Yiheng Shu](https://md.research.elamur.ai/free/en/authors/yiheng-shu/), [Weijian Qi](https://md.research.elamur.ai/free/en/authors/weijian-qi/), Sizhe Zhou and [Yu Su](https://md.research.elamur.ai/free/en/authors/yu-su/) · **Year:** 2025 · **Venue:** arXiv:2502.14802 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2502.14802) · [PDF](https://arxiv.org/pdf/2502.14802) · [DOI 10.48550/arxiv.2502.14802](https://doi.org/10.48550/arxiv.2502.14802) · [arXiv 2502.14802](https://arxiv.org/abs/2502.14802) · [OpenAlex](https://openalex.org/W4407831980)

**Citations:** 2 citations (OpenAlex, 2026-10-14) · [88](https://md.research.elamur.ai/free/en/papers/gutierrez-2025-rag-memory-non-parametric-continual/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Updating an LLM's knowledge through fine-tuning or model editing is expensive and prone to catastrophic forgetting. Retrieval-augmented generation (RAG) has therefore become the usual way to supply new information without changing model weights. Plain vector retrieval, however, handles two things poorly: sense-making over long, complex contexts, and associativity, meaning linking facts across several hops. Structure-augmented systems such as RAPTOR, GraphRAG, LightRAG and HippoRAG target these gaps. The authors show, though, that these systems fall behind strong embedding retrievers on simple factual QA.

HippoRAG 2 keeps HippoRAG's OpenIE-built knowledge graph and Personalized PageRank search, and changes three things:
- it adds passage nodes directly to the graph;
- it matches the query against whole triples rather than only extracted entities;
- it calls an LLM at query time to discard irrelevant triples.

The authors evaluated it on factual, sense-making and multi-hop benchmarks. It beats the strongest baseline, NV-Embed-v2, by about 7 points on associative tasks and matches or slightly exceeds it elsewhere. This makes it a credible step toward non-parametric continual learning for LLMs.

## Key points
- The evaluation covers three memory types: factual (NaturalQuestions, PopQA), sense-making (NarrativeQA) and associativity (MuSiQue, 2Wiki, HotpotQA, LV-Eval).
- In the ablations, linking the query to triples raises Recall@5 by 12.5% on average compared with the original NER-to-node linking.
- The gains hold across dense retrievers. On a MuSiQue subset, passage recall@5 rises from 63.6 to 68.8 (GTE-Qwen2-7B-Instruct), 66.0 to 71.6 (GritLM-7B) and 69.7 to 74.7 (NV-Embed-v2).
- In an incremental-knowledge setup (NQ, MuSiQue), the advantage over NV-Embed-v2 stays stable as data grows. Multi-hop accuracy declines for both systems, while simple QA holds steady.
- The paper was published at ICML 2025, and code and data are available at github.com/OSU-NLP-Group/HippoRAG.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Bernal Jimenez Gutierrez, Yiheng Shu, Weijian Qi, Sizhe Zhou and Yu Su. 2025. From RAG to Memory: Non-Parametric Continual Learning for Large Language Models. arXiv:2502.14802.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{gutierrez2025rag,
  title = {{From RAG to Memory: Non-Parametric Continual Learning for Large Language Models}},
  author = {Bernal Jimenez Gutierrez, Yiheng Shu, Weijian Qi, Sizhe Zhou and Yu Su},
  year = {2025},
  doi = {10.48550/arxiv.2502.14802},
  eprint = {2502.14802},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2502.14802}
}
```

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*Updated 2 November 2026*